The present invention is not limited to electric power, but is described below using electric power as an example. For an electric power company, demand forecasting for the amount of electric power consumption is useful because the cost can be saved by adjustment of the utilization rate of an electric generator set. Thus, typically, usage rates are set such that they are relatively low if an electric power consumed by a facility that consumes an enormous amount of electric power, such as a steel mill, does not exceed an estimate which the facility will have notified the electric power company in advance.
One example of a typical scenario of a steel mill in this case is to set an electric power usage every 30 minutes, to notify an electric power company of that electric power usage, and to decide an electric power usage in the ensuing 30 minutes before 15 minutes elapse. FIG. 1 schematically illustrates the progression of an estimate of an electric power demand in a steel mill and an actual amount of electric power consumption over time. In FIG. 1, a stepped line 102 indicates an electric power demand notified (communicated) to an electric power company, and a curve 104 indicates an actual electric power consumption.
As illustrated in FIG. 1, when the actual electric power consumption 104 exceeds the electric power demand 102 notified to the electric power company, a comparatively high additional fee is imposed according to the excess amount of electric power indicated by the oblique line (called electricity buying). In contrast, when the actual electric power consumption 104 falls below the electric power usage notified, the difference in the electric power usage is unused and thus may preferably be minimized. Accordingly, a system for performing such control using computer processing is desired. Examples of techniques of electric power control disclosed in patent literatures are described below.
Japanese Unexamined Patent Application Publication No. 2002-209335 discloses a customer electric power consumption control and management system that aims to appropriately reduce an electric power consumption in an office building by an energy center managing it over a network. The disclosed system communicates with a building automation system (BAS) in each building, collects measurement data on electric power consumption in each office building from the BAS, predicts the total demand of electric power in the buildings on the basis of total demand prediction ancillary information that contains an electric power consumption history pattern in each office building calculated from the measurement data on electric power consumption in each office building from the past to the present, the measurement data on the electric power consumption in each office building, a weather including temperature and humidity, and information on events in the office buildings, and provides an instruction to the BAS over a network such that the electric power consumption in each office building is controlled on the basis of the predicated total demand of electric power.
Japanese Unexamined Patent Application Publication No. 2003-189477 discloses a power controller that aims to efficiently utilize electric power using a solar cell and a storage battery and also achieve a reduced cost of purchase of electric power. The power controller includes an electric power load indicated so as to include an air-conditioning device, the electric power load being connected to a commercial AC power source line connected to a commercial AC power source, a solar cell connected thereto through an inverter and a DC-to-DC converter in this order, a storage battery connected thereto through a bidirectional inverter and a bidirectional DC-to-DC converter in this order, and a control unit that controls the directivity of the bidirectional inverter.
Japanese Unexamined Patent Application Publication No. 2006-50730 discloses a method and apparatus for creating an operation plan that meets system reliability and operation restrictions of a power generator and that aims to achieve an optimal supply capability of a thermal power plant, a pumped storage power plant, a hydroelectric power plant, an interchange power, and the like. By the method and apparatus, an operation plan that meets all of the constraints is created by relaxation of a start-stop state of a thermal power plant to a real number variable and addition of a restriction of temporal change in the start-stop state, and on the basis of this operation plan, an optimal operation plan is created by establishing the start-stop state of the real number in the start state or the stop state by setting an evaluation function or conducting a local search.
Japanese Unexamined Patent Application Publication No. 2010-268602 discloses a method and apparatus for providing options on electricity tariffs, charging time periods, charging times, or electricity selling in charging and discharging a storage battery. In the disclosed apparatus, a display and input unit receives an input of a constraint for charging and discharging, a system power buying/selling tariff storage unit acquires information on fees of selling electricity and buying electricity, a storage battery charging and discharging control/storage battery status detection unit acquires information on a storage battery, an optimal schedule computation unit generates a schedule that satisfies the constraint on the basis of the constraint, fee information, and storage-battery information, and a charging and discharging control unit charges and discharges the storage battery on the basis of the schedule generated by the optimal schedule computation unit.
Examples of non-patent literatures of the related techniques are described below.
The paper of O. Sundstöm and C. Binding, “Optimization Methods to Plan the Charging of Electric Vehicle Fleets,” Proc. CCPE 2010, pp. 323-328 describes optimization of a charging and discharging plan using mixed integer programming. This technique optimizes a predicted value of an electric power demand as an established value.
The report of J. Goez, J. Luedtke, D. Rajan, and J. Kalagnanam, “Stochastic Unit Commitment Problem,” IBM Research Report, RC24713, 2008 describes optimization of an electric power generation using mixed integer plan. This technique predicts an electric power demand as a plurality of scenarios and assigns a probability to each scenario. However, it is difficult for this technique to deal with many scenarios in terms of computational complexity and to optimize a plan for a long period of time in terms of computational complexity.
The report of D. Nikovski and W. Zhang, “Factored Markov Decision Process Models for Stochastic Unit Commitment,” Technical Report TR2010-083, MITSUBISHI ELECTRIC RESEARCH LABORATORIES, 2010 describes an example that uses the Markov decision process in an electric power generation plan. This example uses the Markov decision process and also uses a result of demand forecasting and deals with optimization of a finite time period.